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source("tianfengRwrappers.R")

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clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
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========================================
circlize version 0.4.13
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
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========================================

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========================================
ComplexHeatmap version 2.2.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.
========================================


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rat Frontier GSE174098 carotid

rat10x <- CreateSeuratObject(Read10X("./rat_scRNAseq/"), names.field = 2, names.delim = "-",
                                   project = "rat", min.cells = 10, min.features = 300) %>%
  PercentageFeatureSet(pattern = "^Mt-", col.name = "percent.mt") 

table(rat10x$orig.ident)
VlnPlot(rat10x,"nCount_RNA") /
VlnPlot(rat10x,"percent.mt") /
VlnPlot(rat10x, "nFeature_RNA")


rat10x <- rat10x %>% subset(subset = nFeature_RNA > 400 & nFeature_RNA < 4000 &
                              nCount_RNA > 1000 &  nCount_RNA < 30000 & percent.mt< 10) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)

mouse10x <- CreateSeuratObject(Read10X("./celldiscovery_mouse_10x/1/"), names.field = 2,
                               names.delim = "-",project = "mouse1", min.cells = 10,
                               min.features = 300) %>%
  PercentageFeatureSet(pattern = "^mt-", col.name = "percent.mt") 

mouse10x2 <- CreateSeuratObject(Read10X("./celldiscovery_mouse_10x/2/"), names.field = 2, names.delim = "-",
                                   project = "mouse2", min.cells = 10, min.features = 300) %>%
  PercentageFeatureSet(pattern = "^mt-", col.name = "percent.mt") 

mouse10x <- merge(mouse10x,mouse10x2)

VlnPlot(mouse10x,"nCount_RNA") /
VlnPlot(mouse10x,"percent.mt") /
VlnPlot(mouse10x, "nFeature_RNA")

mouse10x <- mouse10x %>% subset(subset = nFeature_RNA > 500 & nFeature_RNA < 5000 &
                              nCount_RNA > 1000 &  nCount_RNA < 30000 & percent.mt < 10) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)
# table(mouse10x$orig.ident)


umapplot(mouse10x)
f("Lmo2",mouse10x)

mouse coronary GSE131778

mouse_coronary_countmatrix <- read.csv("./GSE131776_mouse_scRNAseq.txt", sep = "\t")
func <- function(s) {
  paste0(strsplit(s, ".", fixed = T)[[1]][2], "_", strsplit(s, ".", fixed = T)[[1]][1])
}
colnames(mouse_coronary_countmatrix) <- lapply(colnames(mouse_coronary_countmatrix), func) # 拆分样本

mousecor <- CreateSeuratObject(counts = mouse_coronary_countmatrix,
                                   project = "mouse_cor", min.cells = 10, min.features = 300) %>%  PercentageFeatureSet(pattern = "^mt-", col.name = "percent.mt") 

# saveRDS(mousecor,"mousecor.rds")
table(mousecor$orig.ident)
VlnPlot(mousecor,"nCount_RNA") /
VlnPlot(mousecor,"percent.mt") /
VlnPlot(mousecor, "nFeature_RNA")


mousecor <- mousecor %>% subset(subset = nFeature_RNA > 400 & nFeature_RNA < 4000 &
                              nCount_RNA > 1000 &  nCount_RNA < 30000 & percent.mt < 10) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)
table(mousecor$orig.ident)
saveRDS(mousecor,"mousecor.rds")

SMC2

mousecor <- readRDS("mousecor.rds")
f("Prdm16",mousecor, label.size = 7) + theme(legend.text = element_text(size = 20))

umapplot(mousecor_stromal)

Scale for 'colour' is already present. Adding another scale for 'colour', which will
replace the existing scale.

mouse carotid scRNAseq GSE155513

dataload

count_mats <- list.files("./GSE155513_RAW/")
count_mats <- count_mats[count_mats != "sampleinfo.txt"]
allList <- lapply(count_mats, function(file) {
  dd <- read.table(paste0("./GSE155513_RAW/", file), row.names = 1,stringsAsFactors = F)
  colnames(dd) <- as.character(dd['gene',])
  dd <- dd[-1,]
  CreateSeuratObject(
    counts = dd,
    project = file, min.cells = 10, min.features = 300
  )
})
# 合并seurat对象
mouse_carotid <- merge(allList[[1]], 
  y = allList[-1], add.cell.ids = count_mats,
  project = "mouse_carotid"
)
rm(allList)

# saveRDS(mouse_carotid,"mouse_carotid.rds")

process

mouse_carotid <- readRDS("mouse_carotid.rds")

mouse_carotid <- mouse_carotid %>%  PercentageFeatureSet(pattern = "^mt-", col.name = "percent.mt") %>%
subset(subset = nFeature_RNA > 500 & nFeature_RNA < 3000 &
                              nCount_RNA > 1000 &  nCount_RNA < 20000 & percent.mt < 10) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)

saveRDS(mouse_carotid,"mouse_carotid.rds")

SMC

mouse_carotid <- readRDS("mouse_carotid.rds")
umapplot(mouse_carotid)
umapplot(mouse_carotid,group.by = "orig.ident",label = F)
f("Prdm16",mouse_carotid) #SMC
f("Ly6a",mouse_carotid) #SEM-like cells

multi_featureplot(c("Bmp2","Bmp4","Bmp6"),mouse_carotid)

## BMP4 在这里EC的*大部分*中表达，而在人类样本中BMP4+ EC细胞是少数的
markers <- FindAllMarkers(mouse_carotid,logfc.threshold = 0.5,min.diff.pct = 0.2, only.pos = T)

stromal cells

mouse_carotid_stromal <- subset(mouse_carotid,idents = c(0,2,1,7))
mouse_carotid_stromal <- mouse_carotid_stromal %>% RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% FindClusters(resolution = 0.1)
mouse_carotid_stromal <- mouse_carotid_stromal %>% FindClusters(resolution = 0.2)

mouse_carotid_stromal <- readRDS("mouse_carotid_stromal.rds")
# saveRDS(mouse_carotid_stromal,"mouse_carotid_stromal.rds")

umapplot(mouse_carotid_stromal)
f("Dlx2",mouse_carotid_stromal) #Dlx2,Dlx5,Dlx6共同定位

mouse_carotid_stromal <- AddModuleScore(mouse_carotid_stromal, list(mmSMC2_marker), name = "SMC2_score")
f("SMC2_score1", mouse_carotid_stromal,min.cutoff = 0)

multi_featureplot(mmSMC2_marker[1:9],mouse_carotid_stromal,labels = NA, label = F)

分群表

table(group_tab[Dlx5poscells])/table(group_tab) 

GSM4705592_RPS003_matrix.txt.gz GSM4705593_RPS004_matrix.txt.gz GSM4705594_RPS011_matrix.txt.gz GSM4705595_RPS012_matrix.txt.gz GSM4705596_RPS007_matrix.txt.gz 
                    0.080645161                     0.076949502                     0.130000000                     0.029882604                     0.058282209 
GSM4705597_RPS008_matrix.txt.gz GSM4705598_RPS001_matrix.txt.gz GSM4705599_RPS002_matrix.txt.gz GSM4705600_RPS017_matrix.txt.gz GSM4705601_RPS018_matrix.txt.gz 
                    0.004746835                     0.039230575                     0.003976143                     0.093492209                     0.095634096 
GSM4705602_RPS013_matrix.txt.gz GSM4705603_RPS014_matrix.txt.gz GSM4705604_RPS015_matrix.txt.gz GSM4705605_RPS016_matrix.txt.gz 
                    0.066773504                     0.019255456                     0.042503503                     0.030303030 

图

umapplot(mouse_carotid_stromal,label.size = 6,label = F) %>% ggsave("./fig7_mouse/cir_mouse_carotid_stromalumap2.png",plot = ., height = 5, width = 6,device = png)

Scale for 'colour' is already present. Adding another scale for 'colour', which will
replace the existing scale.

XGBoost 跨物种

umapplot(mouse_carotid_stromal, group.by = "scmap_idents")

Scale for 'colour' is already present. Adding another scale for 'colour', which will
replace the existing scale.

# run xgboost wrapper
mousecor_stromal <- XGBoost_predict_from_seuobj(mousecor_stromal, bst_model, 
                                                     celltype_assign = 2)

Warning in XGBoost_predict_from_seuobj(mousecor_stromal, bst_model, celltype_assign = 2) :
  强制改变过程中产生了NA
Warning in XGBoost_predict_from_seuobj(mousecor_stromal, bst_model, celltype_assign = 2) :
  Please ensure that seurat idents are in numeric forms

[1] "ARI = 0.62988464111349"
[1] "return a seurat object with meta.data'X1'~'Xn'"
[1] "return a seurat object with meta.data'projected_idents'"

# run xgboost wrapper
mousecor_stromal <- XGBoost_predict_from_seuobj(mousecor_stromal, bst_model, 
                                                     celltype_assign = 2)

Warning in XGBoost_predict_from_seuobj(mousecor_stromal, bst_model, celltype_assign = 2) :
  强制改变过程中产生了NA
Warning in XGBoost_predict_from_seuobj(mousecor_stromal, bst_model, celltype_assign = 2) :
  Please ensure that seurat idents are in numeric forms

[1] "ARI = 0.62988464111349"
[1] "return a seurat object with meta.data'X1'~'Xn'"
[1] "return a seurat object with meta.data'projected_idents'"

mousecor_stromal <- project2ref_celltype(mousecor_stromal, ds2)
umapplot(mousecor_stromal, group.by = "ref_celltype")

Scale for 'colour' is already present. Adding another scale for 'colour', which will
replace the existing scale.

human ds2

p <- multi_featureplot(c("ACTA2","CNN1","FN1","LUM","VCAM1","LY6A","DLX5","DLX6","LGALS3","SOST"), ds2, labels = NA, label.size = 6)

ggsave("refds2_carotid_stromal.png",plot = p, height = 12, width = 12,device = png)

p <- multi_featureplot(c("FRZB","SOST","DLX5","DLX6"), ds2, labels = NA, label.size = 6)
ggsave("refds2_SMC2_carotid_stromal.png",plot = p, height = 7, width = 7,device = png)

human bulk RNA-seq GSE120521 carotid stable/unstable FPKM

fpkm2tpm <- function(fpkm){
  exp(log(fpkm) - log(sum(fpkm)) + log(1e6))
}

fpkm_matrix <- read.csv("GSE120521_FPKM.csv")
# fpkm_matrix <- distinct(fpkm_matrix) #去除重复行
fpkm_matrix <- fpkm_matrix[!duplicated(fpkm_matrix$name),]
rownames(fpkm_matrix) <- fpkm_matrix$name
fpkm_matrix$name <- NULL

tpm_matrix <- apply(fpkm_matrix, 2, fpkm2tpm)
colSums(tpm_matrix)

group_file <- c("stable","unstable","stable","unstable",
                "stable","unstable","stable","unstable")
boxplot(tpm_matrix, las = 2)

expr_mat <- tpm_matrix[!apply(tpm_matrix, 1, function(x){sum(floor(x) == 0)>3}),]

boxplot(expr_mat, las = 2)

library(limma)
expr_mat <- normalizeBetweenArrays(expr_mat)
expr_mat <- log2(expr_mat+1) #使用log2 scale

#PCA
library(ggfortify) 
df <- as.data.frame(t(expr_mat)) 
df$group <- group_file 
autoplot(prcomp(df[,1:(ncol(df)-1)]), data=df, colour = 'group')+ theme_bw() 

# If the sequencing depth is reasonably consistent across the RNA samples, then the simplest and most robust approach to differential exis to use limma-trend.

fit <- lmFit(expr_mat, group_file)
fit <- treat(fit, lfc=log2(1.2), trend=TRUE)
topTreat(fit, coef=ncol(design))

library(ggpubr)
dat <- expr_mat
design <- model.matrix(~factor(group_file))
fit <- lmFit(dat, design)
fit <- eBayes(fit)
# options(digits = 4)
topTable(fit,coef=2,adjust='BH')
deg <- topTable(fit,coef=2,adjust='BH',number = Inf)
head(deg) 

write.csv(deg,"./datatable/stable vs unstable.csv")
## look up FRZB, SOST, PRDM6, OGN
ds1markers[ds1markers$cluster == "SMC2",]$gene

ds2markers[ds2markers$cluster == "SMC3",]$gene

smc2markers <- intersect(ds1markers[ds1markers$cluster == "SMC2",]$gene, ds2markers[ds2markers$cluster == "SMC3",]$gene)


deg[intersect(smc2markers, rownames(deg)),]

ds1markers[ds1markers$cluster == "SMC1",]$gene

ds2markers[ds2markers$cluster == "SMC1",]$gene

SMC1markers <- intersect(ds1markers[ds1markers$cluster == "SMC1",]$gene, ds2markers[ds2markers$cluster == "SMC1",]$gene)

deg[intersect(SMC1markers, rownames(deg)),]

# logFC_threshold = 1
# pvalue_threshold = 0.05
selected_genes = as.character(read.table("SMC2")$V1)

volcano_plot <- function(filename, selected_genes, logFC_threshold = 1, pvalue_threshold = 0.05)
{
  f<-read.csv("./datatable/stable vs unstable.csv")
  f$threshold <- factor(ifelse(f$adj.P.Val < pvalue_threshold & abs(f$logFC) >= logFC_threshold, 
                              ifelse(f$logFC>= logFC_threshold ,'Up','Down'),'N.S.'),
                       levels=c('Up','Down','N.S.'))
  
   ggplot(f,aes(x=logFC,y=-log10(adj.P.Val),color=threshold))+
    geom_point()+
    scale_color_manual(values=c("#CC0000","#2f5688","#BBBBBB"))+
    geom_text_repel(
      data = f[f$X %in% selected_genes,],
      aes(label = X),
      size = 5, max.overlaps = 1000,
      col="black", segment.color = "black", show.legend = FALSE )+
    theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
          panel.background = element_blank(), axis.line = element_line(colour = "black"))+
    theme(
      axis.title.x = element_text(size = 20), axis.text.x = element_text(size = 15),
      axis.title.y = element_text(size = 20), axis.text.y = element_text(size = 15), 
      legend.text = element_text(size = 20), 
      legend.title = element_blank()
    )+
    ylab('-log10 (p-adj)') +
    xlab('log2 (FoldChange)') +
    geom_vline(xintercept=c(-logFC_threshold,logFC_threshold), lty=3,col="black",lwd=0.5) +
    geom_hline(yintercept =c(0,-log10(pvalue_threshold)),lty=3,col="black",lwd=0.5)
}

selected_genes <- c(as.character(read.table("SMC2")$V1))

p <- volcano_plot("./datatable/stable vs unstable.csv",selected_genes) + ggtitle("stable vs unstable, SMC2 marker")

ggsave("SMC2_stable vs unstable.png",plot = p,device = png,height = 4, width = 6)

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